[ANGLÈS] In this project a musical tone classifier based on a neurally inspired onset model is performed, as many physiological and psychoacoustic evidences reveal the importance of the sound onset in the Human Auditory System, specifically in the fields of coding and recognition of sounds. The human inner ear is simulated: the cochlea is performed with a digital filterbank, the inner hair cells' behaviour is modelled with depressing synapses, and the auditory nerve is emulated with Leaky Integrate-and-Fire (LIF) neurons. After that, the onset spike trains obtained are transformed into a description matrix called onset fingerprint. The database used to test the performance of the system consists in 1020 single-note tones performed with five different instrument's families. The onset fingerprints of all these sounds are used to feed a classification system. Three approaches are made: Classification Trees, Quadratic Discriminant Classifier and Neural Networks. The last one gives a mean success classification rate of 75\%, the same performance as other onset-based classifier methods, but consuming less computing time.